A visualization of the DE values is shown in figure 2. Variation of differential expression in an experiment e,, was estimated by summing the variances of (logarithm of) treatment and control gene expressions.
In this case, data tracking gene expression in the course of an experiment can be used to identify the most pronounced putative mechanisms.
The probabilistic nature of the model allows for a precise formulation of retrieval, in which the model is queried with the differential expression in gene sets of an experiment and it returns a ranked list of relevant experiments.
Common tools to process RNA-Seq data, such as IGV [ 10], SAMtools [ 11], Tablet [ 12], and Bambino [ 13], focus on aligning individual reads with the genome, because the number of reads aligned with particular genes can be used as a metric to quantify differential gene expression within the context of an experiment.
It is well known that most probesets perform consistently and reliably, in that similar estimates of expression are obtained from two replicates of an experiment.
Although RNA interference can reduce the expression of a gene, interpretation of such experiments can be unreliable because of non-specific targeting or incomplete inactivation of genes.
In contrast, in our experiment, differential expression of a gene is inferred from a change in its overall expression level, not by allele-specific expression.
This is not a practical experiment, as expression of a construct requires several days post-retroviral injection, thus assay at 3 days would not be interpretable.
We conclude from these experiments that expression of a toxic form of SOD1 increases the proportion of heterochromatin in Aptx−/− neurons.
We also looked at the expression of the gyrase genes from an experiment in which synchronised MM2d cells were allowed to progress through the cell cycle [27].
As control experiments, expression of myosin, an intracellular protein, was checked (Fig. 5A).
